A medical image diagnosis apparatus is an imaging device such as Positron emission tomography (PET) and X-ray computed tomography imaging apparatus (X-ray CT apparatus). The medical imaging apparatus detects radiation incident to a plurality of detector elements to obtain medical data, and generates a medical image by reconstruction process using the medical data. For example, PET is an imaging device in nuclear medicine based on the use of a weak radioactively marked pharmaceutical (a tracer) in order to image certain features of a body. PET images display the spatial distribution of the radiopharmaceutical enabling a doctor or clinician to draw conclusions about metabolic activities or blood flow, for example. Therefore, PET is a functional imaging technique that has applications in oncology, cardiology, and neurology, e.g., for monitoring tumors or visualizing coronary artery disease.
In PET imaging, a tracer agent is introduced into the patient to be imaged via injection, inhalation, or ingestion. After administration, the physical and bio-molecular properties of the agent cause it to concentrate at specific locations in the patient's body. The actual spatial distribution of the agent, the intensity of the region of accumulation of the agent, and the kinetics of the process from administration to its eventual elimination are all factors that may have clinical significance.
During this process, a tracer attached to the agent will emit positrons. When an emitted positron collides with an electron, an annihilation event occurs, wherein the positron and electron are combined. Most of the time, an annihilation event produces two gamma rays (at 511 keV) traveling at substantially 180 degrees apart.
To reconstruct the spatio-temporal distribution of the tracer via tomographic reconstruction principles, each detected event is characterized for its energy (i.e., amount of light generated), its location, and its timing. By detecting the two gamma rays, and drawing a line between their locations, i.e., the line-of-response (LOR), one can determine the likely location of the original disintegration. While this process will only identify a line of possible interaction, by accumulating a large number of those lines, and through a tomographic reconstruction process, the original distribution can be estimated. The collection of a large number of events creates the necessary information for an image of a patient to be estimated through tomographic reconstruction.
Tomographic reconstruction has been widely applied to visualizing the anatomical information of patients. Tomographic reconstruction can be used in various modalities, including projection-based imaging, such as in X-ray computed tomography (CT), and emission-based imaging, such as in PET. Due to health concerns regarding exposure to radiation, doctors, scientist, and engineers in medical imaging strive to maintain radiation doses as low as reasonably possible. This effort to maintain radiation doses as low as reasonably possible motivates continued improvements in reconstructed image quality while decreasing the radiation doses and signal-to-noise ratios of the measured signals. Other constraints, in addition to concerns of dose level, motivate improvements in the computational efficiency and speed of reconstruction algorithms. For example, economic concerns and market forces motivate improvements to more efficiently employ computational resources, and desires for close to real-time feedback during surgical procedures, for example, can motivate efforts to minimize the lag time between measurements and the generation of the reconstructed image.
Statistical image reconstruction algorithms in tomography can provide improved image quality at reduced dose levels relative to more conventional reconstruction methods like filtered back-projection (FBP).
However, the statistical approach used in image reconstruction of PET imaging and X-ray CT imaging takes time due to requiring a huge amount of computational processing. To remedy the slow computationally intensive operation of standard statistical reconstruction approaches, improved methods using iterative algorithms for statistical reconstruction that converge more quickly in fewer iterations are desired.